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All Journal JOIV : International Journal on Informatics Visualization Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Hamida, Silfiana Nur
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Computer Science & IT Engineering

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Spatial Mapping of Landslide Susceptibility Level in Pacitan District Using Analytical Hierarchy Process and Natural Break Fariza, Arna; Basofi, Arif; Hamida, Silfiana Nur
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i1.8619

Abstract

Pacitan district has a high potential for landslides. Landslide is a hydrometeorological disaster that causes loss of life, property loss, and environmental damage. Disaster preparedness is very necessary for the wider community in dealing with landslide emergency response situations. Applications to determine the level of vulnerability to landslides are very useful to minimize the impact and losses on the Pacitan community. This study aims to make an application for assessing the level of landslide susceptibility using the analytical hierarchy process and natural break based on the factors that cause landslides in the sub-district or village of Pacitan district. The factors that cause landslides in Pacitan district consist of weather, history of landslides, land slope, and history of earthquakes. The results of the AHP and natural break classifications are visualized in the form of a spatial map into 3 categories, namely high, medium and low vulnerability levels. The results of the AHP classification and natural break in the 2016-2020 data have a good average GVF value of 0.77. This shows that in general, the results of the 2016-2020 data classification are correct. Mobile device-based applications provide convenience for the public in accessing information as an effort to improve landslide disaster preparedness.
Spatial-Temporal Visualization of Dengue Fever Vulnerability in Kediri Using Hierarchy Clustering Based on Mobile Devices Hamida, Silfiana Nur; Fariza, Arna; Basofi, Arif
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2195

Abstract

In Indonesia, Dengue Fever (DF) is a contagious disease that is a significant issue in public health. The Kediri Regency in East Java, as reported by the Ministry of Health in 2019, had the highest number of DF cases. If not addressed promptly, DF can lead to outbreaks, creating a health emergency. The lack of a thorough investigation into the diversity of risk within a spatial and temporal region exacerbates this issue. Therefore, spatial-temporal analysis is crucial in developing a warning system to prevent and control DF. This paper proposes a method that combines the Euclidean Distance calculation with the Hierarchical Clustering method. We collected data from the Kediri Regency health department and conducted pre-processing and classification processes, considering the number of DF victims, death rate, population, rainfall, and public facilities. The hierarchical clustering algorithm was used to categorize the 344 village analyses into low, medium, and high vulnerability categories. This method allows for a comparison of yearly single, average, complete, and centroid linkage in DF vulnerability levels. We also employed spatial-temporal visualization based on cellular applications to create a clear picture of areas vulnerable to DF. The experimental results in clustering showed a satisfactory level of matching, with variant values calculated using the hierarchical clustering method. The variants for single linkages were 0.113; for average linkages, they were 0.120; for complete linkages, they were 0.178; and for centroid linkages, they were 0.106. The grouping validation results indicated that the centroid linkage method produced the best variant level. We suggest further enhancing the methods with better process steps using other pre-processing methods to improve the validation quality.
Co-Authors Arna Fariza Basofi, Arif